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What an AI agent really is

The word “agent” is used to sell everything. Underneath sits a fairly simple loop: a model picks an action, a program runs it, a result comes back. What matters is the limits you put around it.

Latest articles

Development

Measuring a website: Core Web Vitals without the jargon

Three measures summarise what a visitor feels: how fast the main content appears, how responsive the page is to interaction, how stable the layout is. What they mean, how to measure them, and where to start.

The four sections

  • AI

    Agents, document search, choosing models: how these systems work, and where they fail.

  • Development

    Designing and measuring web applications: performance, architecture, practices that last.

  • Cloud & DevOps

    Deploying and operating: Kubernetes, GitOps, continuous delivery, at the scale of a small team.

  • Security

    Threats, hardening, supply chain: what really protects, and what only reassures.

More to read

  1. 2026

    Development

    RAG: making a model answer from your own documents

    A model has not read your documents. RAG means finding the right passages before you ask it the question. Chunking, embeddings, hybrid search, citations: where quality is won and where it is lost.

  2. 2026

    AI

    Open models or closed APIs: how to choose

    Host a model yourself or pay for an API: the right answer depends on five criteria you can examine one by one. Real cost, privacy, latency, licence and dependency, without dogma.

  3. 2026

    Security

    Prompt injection: the flaw agents have to learn to live with

    A model does not firmly tell an instruction from a piece of data: it is all text. As long as that holds, an agent reading outside content can be hijacked. You cannot remove the flaw, you can bound what it can cause.

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